Energy-Efficient Federated Learning for AIoT using Clustering Methods

TL;DR

This paper investigates energy consumption in federated learning for AIoT, proposing clustering methods to improve convergence and reduce energy use by grouping similar devices, validated through extensive experiments.

Contribution

Introduces clustering-informed device selection methods that enhance convergence speed and energy efficiency in federated AIoT systems, addressing heterogeneity challenges.

Findings

Clustering methods improve convergence rates.

Energy consumption is reduced with proposed strategies.

Methods outperform recent approaches in experiments.

Abstract

While substantial research has been devoted to optimizing model performance, convergence rates, and communication efficiency, the energy implications of federated learning (FL) within Artificial Intelligence of Things (AIoT) scenarios are often overlooked in the existing literature. This study examines the energy consumed during the FL process, focusing on three main energy-intensive processes: pre-processing, communication, and local learning, all contributing to the overall energy footprint. We rely on the observation that device/client selection is crucial for speeding up the convergence of model training in a distributed AIoT setting and propose two clustering-informed methods. These clustering solutions are designed to group AIoT devices with similar label distributions, resulting in clusters composed of nearly heterogeneous devices. Hence, our methods alleviate the heterogeneity often encountered in real-world distributed learning applications. Throughout extensive numerical experimentation, we demonstrate that our clustering strategies typically achieve high convergence rates while maintaining low energy consumption when compared to other recent approaches available in the literature.